Joint Noise Level Estimation from Personal Photo Collections

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Abstract

Personal photo albums are heavily biased towards faces of people, but most
state-of-the-art algorithms for image denoising and noise estimation do not exploit
facial information. We propose a novel technique for jointly estimating noise
levels of all face images in a photo collection. Photos in a personal album are
likely to contain several faces of the same people. While some of these photos
would be clean and high quality, others may be corrupted by noise. Our key idea is
to estimate noise levels by comparing multiple images of the same content that
differ predominantly in their noise content. Specifically, we compare geometrically
and photometrically aligned face images of the same person.

Our estimation algorithm is based on a probabilistic formulation that seeks to
maximize the joint probability of estimated noise levels across all images. We
propose an approximate solution that decomposes this joint maximization into a
two-stage optimization. The first stage determines the relative noise between
pairs of images by pooling estimates from corresponding patch pairs in a
probabilistic fashion. The second stage then jointly optimizes for all absolute
noise parameters by conditioning them upon relative noise levels, which allows
for a pairwise factorization of the probability distribution. We evaluate our
noise estimation method using quantitative experiments to measure accuracy on
synthetic data. Additionally, we employ the estimated noise levels for automatic
denoising using "BM3D", and evaluate the quality of denoising on real-world
photos through a user study.